A library for developing real-time and embedded applications in C

Val, Pablo Basanta; García-Valls, Marisol

doi:10.1016/j.sysarc.2015.03.003

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Also, we are extending the proposed techniques to larger cluster infrastmctw·es equipped with ente1prise inachines. Finally we explore the integration extending results given in [26] and [44][45][46][47] for distributed stream processing as a building block for IBIDAE.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Industrial Big-Data Engine

Val

2018

IEEE Trans. Ind. Inf.

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Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Abstract-Current trends in industrial systems opt for the use of different big-data engines as a mean to process huge amounts of data that cannot be processed with an ordinary infrastructure. The number of issues an industrial infrastructure has to face is large and includes challenges such as the definition of different efficient architecture setups for different applications, and the definition of specific models for industrial analytics. In this context, the article explores the development of a medium size big-data engine (i.e. implementation) able to improve performance in map-reduce computing by splitting the analytic into different segments that may be processed by the engine in parallel using a hierarchical model. This type of facility reduces end-to-end computation time for all segments with their results then merged with other information from other segments after their processing in parallel. This type of setup increases performance of current clusters improving I/O operations remarkably as empirical results revealed.

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Section: Discussionmentioning

confidence: 99%

An Efficient Industrial Big-Data Engine

Val

2018

IEEE Trans. Ind. Inf.

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“…In essence, the model mixes two main technologies: Apache Storm [19] which is an efficient approach for sub-second delays, and Apache Spark [24], which offers a rich and an efficient framework on which to implement offline map-reduce applications. The model is also highly inspired on previous architectures described in the context of distributed real-time systems for Java [20] [21][25] [53]. From this distributed context, the architecture also takes specific support for parallel processing [9], and several online scheduling mechanisms for predictable reconfiguration strategies (see [26]).…”

Section: Security and Privacymentioning

confidence: 99%

Architecting Time-Critical Big-Data Systems

Val

Audsley

Wellings

et al. 2016

IEEE Trans. Big Data

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Abstract-Current infrastructures for developing big-data applications are able to process -via big-data analytics-huge amounts of data, using clusters of machines that collaborate to perform parallel computations. However, current infrastructures were not designed to work with the requirements of time-critical applications; they are more focused on general-purpose applications rather than time-critical ones. Addressing this issue from the perspective of the real-time systems community, this paper considers time-critical big-data. It deals with the definition of a time-critical big-data system from the point of view of requirements, analyzing the specific characteristics of some popular big-data applications. This analysis is complemented by the challenges stemmed from the infrastructures that support the applications, proposing an architecture and offering initial performance patterns that connect application costs with infrastructure performance.

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“…In the literature there is various research related to the implementation of CNC machines and low-cost using embedded systems [12][13][14][15]. In [16], the authors implemented a control system for micro-drilling PCBs.…”

Section: Introductionmentioning

confidence: 99%

Printed Circuit Board Drilling Machine Using Recyclables

2018

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The implementation of a printed circuit board (PCB) drilling machine using recyclable materials and computer-aided control is presented. A mechanical system using a DC motor for movement on the X and Y axes, and a transmission mechanism by belts, pulleys, and a worm screw was made. For the Z axis, a mechanism based on a worm screw, nuts, and a stepper motor was implemented. The main board has two microcontrollers communicating in a master-slave configuration via a serial protocol. A real-time operating system (OSA) was implemented to optimize the data flow to the computer using the USB protocol, for communication with the slave microcontroller, positioning the Cartesian axes, and control the motors. The slave is responsible for monitoring the status of the encoders and limit switches, as well as the information delivery to the master. A Matlab-based user interface was developed to determine the coordinates of the holes to be drilled by processing a jpg image. This also allows the user to control the DC motors using PWM signals via configurable parameters of PID controllers. The end result is a drilling machine which able to operate both manually and via a computer, for drilling PCBs of a maximum size of 24 × 40 cm.

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A library for developing real-time and embedded applications in C

Cited by 8 publications

References 36 publications

An Efficient Industrial Big-Data Engine

An Efficient Industrial Big-Data Engine

Architecting Time-Critical Big-Data Systems

Printed Circuit Board Drilling Machine Using Recyclables

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